Study of shrimp recognition methods using smart networks

Traditional shrimp recognition algorithms, based on machine vision, commonly utilize human-designed features, which are heavily dependent on human experience and can be inefficient and inaccurate. A smart deep convolutional neural network, using the improved LeNet-5 structure (ShrimpNet), is proposed to address this problem. Shrimp image segmentation, normalization and data augmentation were initially performed. Given the morphological differences in the external features of shrimp, the LeNet-5 structure was modified into a three-layer parallel structure for efficient matching and identification. A combination classifier strategy was subsequently added into the fully connected layers to strengthen the feature expression in the corresponding classes. Finally, different architectures were explored by shrinking the depth and width to search for effective network structures that could act as alternatives for practical applications and reveal the practical use of ShrimpNet. Experimental results revealed that the smaller model (ShrimpNet-3) could achieve a validation accuracy of 96.84% and a modeling time of 0.47 h for the constructed dataset. Therefore, the proposed method is promising for shrimp classification and quality measurement of production lines.